Although any neural networks can be used, in this paper we adopt the basic U-Net [1]} to learn degradations in historical documents,
similar to image segmentation.
The architecture consists two paths: contracting path and expansive path.
In the contracting path, there are five convolutional layers with the kernel size 3\(\times \) 3, each followed by a leaky-ReLU [2]} (\(\lambda =0.25\) ) and 2\(\times \) 2 max pooling layer with stride 2.
In the expansive path, the deconvoluational operation is used to upsample the feature maps and then it is concatenated with the corresponding feature maps on the contracting path, followed by a convolutional layers.
The filter numbers in five convolutional layers are set to: [16,32,64,128,256], respectively.
The output layer has the same size as the input image, which makes the additive operation possible (shown in Eqn. REF ).
